预测和解释COVID-19之前和期间医院患者的缺勤风险
Ana Borges1, Mariana Carvalho1, Miguel Maia1
1CIICESI, ESTG, Politecnico do Porto, Rua do Curral, Casa do Curral, Margaride, Felgueiras, 4610-156, Portugal.
概括
这项研究使用预测模型确定了患者缺勤风险因素,并用CART算法解释了它们. 调查结果显示,在COVID-19大流行期间以及在不同医院专科中,患者缺勤情况会发生变化.
科学领域:
- 健康管理健康管理
- 在医疗保健中的数据科学.
- 预测分析是一种预测分析.
背景情况:
- 没有事先通知的患者缺勤在医院管理中构成了重大挑战.
- 了解导致这种现象的因素对于提高运营效率和患者护理至关重要.
研究的目的:
- 分析与患者缺勤相关的风险因素.
- 使用修改的CART算法,开发一个可解释的患者缺勤模型.
- 为了比较COVID-19大流行之前和期间的患者缺勤情况.
主要方法:
- 利用了来自北葡萄牙的真实医院数据.
- 应用了经过验证的预测模型来推断出缺勤风险因素.
- 开发并实施了对可解释AI进行修改的分类和回归树 (CART) 算法.
主要成果:
- 确定了患者缺勤的关键风险因素.
- 可解释模型提供了人类可解释的关于缺勤的见解.
- 在疫情前和疫情期间,观察到患者缺勤情况的显著差异.
- 在不同的医院专科中,缺勤概况也各不相同.
结论:
- 这项研究为患者缺勤动态提供了有价值的见解.
- 开发的可解释模型可以帮助医院制定主动管理策略.
- 显然,COVID-19大流行已经改变了患者的缺勤行为,需要适应性管理方法.
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